BlockingQueue<E> q, Collection<? super E> buffer, int numElements, Duration timeout)
      throws InterruptedException {
    return drain(q, buffer, numElements, toNanosSaturated(timeout), NANOSECONDS);
  }

  /**
   * Drains the queue as {@link BlockingQueue#drainTo(Collection, int)}, but if the requested {@code
   * numElements} elements are not available, it will wait for them up to the specified timeout.
   *

      BlockingQueue<E> q, Collection<? super E> buffer, int numElements, Duration timeout)
      throws InterruptedException {
    return drain(q, buffer, numElements, toNanosSaturated(timeout), NANOSECONDS);
  }

  /**
   * Drains the queue as {@link BlockingQueue#drainTo(Collection, int)}, but if the requested {@code
   * numElements} elements are not available, it will wait for them up to the specified timeout.
   *

      BlockingQueue<T> q,
      Collection<? super T> buffer,
      int numElements,
      long timeout,
      TimeUnit unit,
      boolean interruptibly)
      throws InterruptedException {
    return interruptibly
        ? Queues.drain(q, buffer, numElements, timeout, unit)
        : Queues.drainUninterruptibly(q, buffer, numElements, timeout, unit);
  }

  public void testMultipleProducers() throws Exception {

 public:
  explicit TapeVSpace(AbstractContext* ctx) : ctx_(ctx) {}
  ~TapeVSpace() override {}

  // Returns the number of elements in the gradient tensor.
  int64_t NumElements(AbstractTensorHandle* tensor) const override;

  // Consumes references to the tensors in the gradient_tensors list and returns
  // a tensor with the result.
  AbstractTensorHandle* AggregateGradients(

     *
     * @param <E> the element type of {@link ArrayDeque}
     * @param numElements the lower bound of the initial capacity range for the deque
     * @return a new instance of {@link ArrayDeque}
     * @see ArrayDeque#ArrayDeque(int)
     */
    public static <E> ArrayDeque<E> newArrayDeque(final int numElements) {
        return new ArrayDeque<>(numElements);
    }

    /**

  if (h == nullptr) {
    status->status = tensorflow::errors::InvalidArgument("Invalid handle");
    return -1;
  }

  int64_t num_elements = -1;
  status->status = tensorflow::unwrap(h)->NumElements(&num_elements);
  return num_elements;
}

int64_t TFE_TensorHandleDim(TFE_TensorHandle* h, int dim_index,
                            TF_Status* status) {
  if (h == nullptr) {

    Tensor output;
    ASSERT_EQ(absl::OkStatus(), TF_TensorToTensor(dst, &output)) << line;
    ASSERT_EQ(src.NumElements(), output.NumElements()) << line;
    for (int64_t i = 0; i < src.NumElements(); ++i) {
      ASSERT_EQ(data[i], output.flat<tstring>()(i)) << line;
    }

    TF_DeleteTensor(dst);
  }
}

TEST(CAPI, TensorEncodeDecodeStrings) {
  TestEncodeDecode(__LINE__, {});

    status->status = result;
    return;
  }

  // Store results in c_outputs[]
  for (int i = 0; i < noutputs; ++i) {
    const Tensor& src = outputs[i];
    if (!src.IsInitialized() || src.NumElements() == 0) {
      c_outputs[i] =
          EmptyTensor(static_cast<TF_DataType>(src.dtype()), src.shape());
      continue;
    }
    c_outputs[i] = TF_TensorFromTensor(src, &status->status);